Intravenous thrombolytic therapy (TT) improves outcomes in acute ischemic stroke (AIS), when delivered within 3 hours of symptom-onset Yet, despite the overall benefits, there remains a significant risk of thrombolytic-related intracranial hemorrhage (ICH), even within the 3-hour time frame. And multiple trials including patients beyond this time-window have failed to find benefit for TT in AIS. In part because of the narrow time-window of therapeutic opportunity, less than 5% of all AIS patients are currently treated with TT. Our preliminary work has demonstrated that the absence of overall benefit from TT when administered after 3 hours from symptom-onset obscures the fact that some patients are still likely to benefit, while others are more likely to be harmed. Further, these patient sub-groups can be identified on the basis of pre-treatment clinical characteristics, but only when multiple characteristics are considered simultaneously. Based on our mathematical models that identify patients with AIS who have a favorable risk-benefit profile for TT, and working closely with experienced human factors engineers and the ultimate end-users (ie stroke neurologists and emergency physicians), we propose to develop a set of tools incorporated into a computer-based decision-support instrument (CDS1) for use in real time. The core component of this CDSI is the patient-selection module, to help select patients with a favorable risk-benefit profile for TT. Thus, the specific aims of this project are: 1. To incorporate our developed mathematical models for patient-selection into a usable CDSI. 2. Through an iterative design cycle, including several phases of usability testing, to develop this prototype into a comprehensive decision-support instrument with a well-designed a user interface, well-integrated into usual work-flow, and supporting a range of functions important for acute stroke care. 3. To plan a pilot study to evaluate the feasibility of "real-time assisted multi-dimensional patient selection" to select AIS patients who may benefit from TT when treated more than 3 hours from symptom-onset. While the results of this study should have important implications for the use of TT in AIS, we anticipate that this new method of "assisted multi-dimensional patient-selection" may have profound implications in many areas, especially for treatments in which the risks and the benefits are finely balanced.